Classifier training

1. Figure 1 DIGE analysis Cluster analysis ELISA assays Prediction analysis (LDA) Two class classification 1 3 2 Predictor discovery in training set Predictor test in testing set DIGE raw gel images SJIA (10 F, 10 Q) POLY (5 F, 5 Q) 2d hierarchical clustering heatmap plotting Test to anticipate SJIA F in Q Classification analysis Assay development 4 5 9 7 8 randomization randomization Training set SJIA (12 F, 12 Q) POLY (13 F, 10 Q) Testing set SJIA (10 F, 10 Q) POLY (10 F, 5 Q) ELISA assay FC (27) KD (10) ELISA assay Q->F (11) Q->Q (14) Training set SJIA F(12) KD (7), FC (15) Testing set SJIA F(10) KD (3), FC (12) Mann Whitney Spot finding spot alignment feature extraction P < 10-5 7 ELISA assays 8 protein candidates Normalization manual review LDA Fisher exact test Classifier training Blind testing Classifier training Blind testing ELISA assay Clustering Box-and-Whisker Analysis 889 discrete spot features Fisher exact test Fisher exact test Discriminate SJIA F SJIA Q POLY F POLY Q Discriminate SJIA F KD FI Manual review Classify SJIA F vs Q POLY F vs Q Classify SJIA F vs Q POLY F vs Q Classify SJIA F vs Non SJIA F Classify SJIA F vs Non SJIA F MSMS ID 96 spots

2. SJIA F Q Figure 2 SJIA POLY POLY F Q F Q F Q A B C

3. SJIA F Q Figure 2 SJIA POLY POLY F Q F Q F Q A B C D Protein quantity 4 SAP SAP SJIA POLY 3 F Q F Q SAA SAA MRP8 MRP8 MRP14 MRP14 2 HP HP 1 CRP CRP 0 APOA1 APOA1 A2M APOA1 CRP HP MRP14 MRP8 SAA SAP A2M A2M

4. Figure 3 E B A C D Training set n = 24 Testing set n = 20 Training Testing SJIA F vs. Q SJIA • Biomarker panel • of 7 members • A2M • APO AI • CRP • HP • MRP8/MRP14 • SAA • SAP SJIA Clinical diagnosis Clinical diagnosis SJIA F SJIA Q SJIA F SJIA Q F Q F Q 12 12 10 10 n = n = Sensitivity LDA Testing Classified as F Classified as F 11 1 8 3 Predicted probabilities Classified as Q Classified as Q 1 11 2 7 ESR : AUC=0.86 91.6% 91.6% 80% 70% Percent Agreement with clinical diagnosis + - Percent Agreement with clinical diagnosis + - panel : AUC=0.82 91.6% 75% CRP : AUC=0.58 Overall P = 1.0 X 10-3 Overall P = 7 X 10-2 Patient samples 1- Specificity

5. Figure 4 D A B C Training set n = 23 Testing set n = 15 Training Testing POLY F vs. Q POLY POLY Clinical diagnosis Clinical diagnosis PF PQ PF PQ F Q F Q 13 10 10 5 n = n = Sensitivity LDA Testing Classified as F Classified as F 13 1 9 0 Predicted probabilities Classified as Q Classified as Q 0 9 1 5 ESR : AUC=0.64 100% 90% 90% 100% Percent Agreement with clinical diagnosis + - Percent Agreement with clinical diagnosis + - panel : AUC=0.90 95.6% 93.3% CRP : AUC=0.88 Overall P = 1.2X 10-5 Overall P = 2 X 10-3 Patient samples 1- Specificity

6. Figure 5 C A B Training set n = 25 SJIA QF vs. QQ SJIA Training Clinical diagnosis QF QQ QQ QF 11 14 n = Sensitivity LDA Predicted probabilities Classified as QF 6 2 Classified as QQ 5 12 ESR : AUC=0.60 54.5% 85.7% Percent Agreement with clinical diagnosis + - panel : AUC=0.78 72% CRP : AUC=0.59 Overall P = 0.08 Patient samples 1- Specificity

7. Figure 6 B A C SJIA F KD FC SJIA F Data set n = 34 KD FC 4 10 12 12 SJIA F KD FC SJIA F NOT-SJIA F Clinical diagnosis 3 10 24 n = SAP Unsupervised clustering SAA Protein quantity 2 MRP8 MRP14 Clustered as SJIA F 7 1 1 Clustered as NOT-SJIA F HP 3 23 CRP 70% 95.8% - Percent Agreement with clinical diagnosis + 0 APOA1 88.2% Overall P = 1.6 X 10-4 A2M A2M APOA1 CRP HP MRP14 MRP8 SAA SAP

8. Figure 7 A B C Training set n = 34 Testing set n = 25 SJIA F SJIA F KD FC Training Testing KD FC 10 3 12 12 7 15 FC KD SJIA F FC KD SJIA F SJIA F NOT SJIA F SJIA F NOT SJIA F Clinical diagnosis Clinical diagnosis 12 22 10 15 n = n = LDA Testing Predicted probabilities Clustered as SJIA F 12 0 9 0 Clustered as NOT SJIA F 0 22 1 15 100% 100% 90% 100% Percent Agreement with clinical diagnosis + - Percent Agreement with clinical diagnosis + - Patient samples 100% 93.3% Overall P = 7.4X 10-7 Overall P = 4.9 X 10-6